Data Analytics Method For Detecting Extinction Precursors To Lean Blowout In Spray Flames

ABSTRACT

Aircraft engines must always maintain a margin between the operating equivalence ratio and the lean blowout boundary because flame-out presents a significant risk to the safety of the aircraft. It is believed that flames undergo a series of extinction/re-ignition phenomena before blowout. Previous attempts to characterize these phenomena have not been universally accepted. The approach presented here is from data analytics and consists of three parts: data curation, fault detection, and an adaptive alarm reliability assessment. The data curation filters the nonstationary behavior from photomultiplier tube signals recorded from a combustor test-rig, thereby reducing the number of false alarms. The filtered data is used to develop a fault detection algorithm that detects changes in the statistical properties of the signal. This results in alarms that serve as precursors of impending blowout. By leveraging information from previous blowout occurrences and the currently observed signal, the reliability of these alarms is updated in an adaptive manner. Through this methodology, combustion system operators are provided a means for assessing the proximity of blowout in a probabilistic manner.

KEYWORDS:

• Lean blowout
• extinction
• re-ignition
• time series modeling
• control charts

Acknowledgments

This work was supported by the U.S. Department of Energy through the National Energy Technology Laboratory-University Turbine Systems Research program (#DE-FE0031288) monitored by Omer Bakshi, the Department of Defense through Air Force Office of Scientific Research (contract #FA9550-16-1-0442) under the supervision of Dr. Chipling Li, and by the Department of Transportation through ASCENT via the Federal Aviation Administration Center of Excellence for Alternative Jet Fuels and the Environment (#13-C-AJFE-GIT-008) under the supervision of Cecilia Shaw. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any of these federal agencies.

Declaration of interest

None

Additional information

Funding

This work was supported by the Air Force Office of Scientific Research [#FA9550-16-1-0442]; Federal Aviation Administration [#13-C-AJFE-GIT-008]; U.S. Department of Energy [#DE-FE0031288].